Computing (Software Engineering)

Examine the tools and techniques that software engineers use on a daily basis to successfully build and maintain large software systems.

Demonstrate independence and originality on an individual project, specialising in an area of software engineering.

Unpick the key concepts, problems and results in distributed algorithms and gain practical experience of programming them.

Study network and web security broadly from the network to the application layer and understand the main threats, attack techniques and defences relevant to cybersecurity and network security.

Analyse how performance engineering ensures computer systems are responsive, scalable, and efficient and explore the fundamental principles and techniques used to achieve these goals.

Deepen your knowledge of the themes and challenges of host-level native, web, and mobile security and privacy.

Discover how cryptographic techniques can be used to design and implement secure communicating systems for a variety of different needs and applications.

Analyse the foundational principles behind decentralised ledgers and apply them to current research in cryptocurrencies.

Become familiar with the concepts and techniques underlying privacy-enhancing technologies across a variety of areas.

Learn how to design program analyses for classical and probabilistic programs and how to solve the resulting equations and constraints.

Use separation logic to reason compositionally about sequential and concurrent heap-manipulating programs.

Advance your knowledge of the concepts that enable the design and implementation of scalable distributed systems.

Assess techniques and tools for improving the reliability of software systems and discuss their respective strengths and weaknesses.

Develop intellectual and practical skills in the use of modal logics for knowledge representation and automated reasoning in artificial intelligence.

Assess the best decision algorithms for solving real-life problems arising in computer science, computational management and economics.

Understand the rationale behind the theoretical end of computational neuroscience and basic principles of the tools needed to simulate the brain's intelligent behaviour in this interdisciplinary module.

Gain a flavour of different aspects of the broad and ever-expanding field of graph theory and learning, including conventional graph data analysis methods and the nascent field of graph neural networks.

Explore the intersection of robotics and human-computer interaction, covering user-centric and user study design, data analysis, and relevant theoretical foundations, in this exciting new field.

Delve into the deep connections between information theory, statistics and machine learning, in this module linking computing, statistics and geometry. Includes mathematical and computational exercises, and provides crucial theoretical background for a career in data science or machine learning.

Implement and operate a simplified machine learning-based system in this module covering engineering concepts underpinning trustworthy systems. Looks at all aspects of systems, including data ingestion, user experience, regulation and wider society.

Learn to build machine learning models that work with non-Euclidean data.

You will study embeddings, optimization techniques from Riemannian geometry, and graph theory, and apply neural networks designed for data on curved spaces and manifolds.

Learn how modern networked systems are built and gain hands-on experience with their design and implementation. You will explore new architectures, hardware and software infrastructure, and core principles such as layering and the end-to-end argument.

Assess the exciting field of mobile robotics, at a time when cutting-edge robots are beginning to leave the research laboratory to tackle real-world tasks.

Develop a thorough understanding of high-performance and energy-efficient computer architecture as a basis for informed software performance engineering.

Understand basic concepts of computer graphics and familiarise yourself with the mathematical principles used for computer generated imagery, shading and light approximations.

Examine the approaches that enable the rapid and systematic design of custom computers and learn how to analyse the performance of a custom computer.

Gain an in-depth understanding of the state-of-the-art of logic-based learning, starting from its key foundation concepts and principles before moving to more recent advances.

Assess modern techniques in realistic computer graphics and image synthesis, particularly image-based techniques for photorealism.

Discover how to describe the complexity classes associated with computational problems, and how to fit a particular problem into a class of related problems.

Understand the basic concepts of quantitative finance and financial engineering and build awareness of the major decision, hedging, and pricing problems in finance.

Develop a deeper understanding of optimal decision making models, algorithms and applications to engineering, finance, and machine learning.

Explore the fundamental concepts and advanced methodologies of deep learning and relate them to real-world problems.

Explore the fundamental concepts and advanced methodologies of machine learning for imaging and relate them to real-world problems in computer vision and medical image analysis.

Learn how to design and implement modern statistical machine learning methodologies, as well as inference mechanisms.

Acquire the techniques and tools needed to devise and develop natural language processing (NLP) components and applications.

Examine how probability can be used to make decisions by a computer and advance your understanding of inference networks and linear and non-linear methods in statistical pattern recognition.

Explore the role of quantum computers in providing efficient solutions to problems that are currently computationally intractable

Learn how to describe the core principles of autonomous systems learning and calculate mathematical solutions to problems using reinforcement learning theory.

Study an advanced computer science topic of your choice and plan, write and present a scientific report.

Gain insight into the different types of problems that exist in machine learning and the basic algorithms used to address them.

Explore how images are formed, how they are represented on computers and how they can be processed by computers to extract semantic information.

Engage with the emerging field of robot learning and discover how robots can acquire skills and control their bodies using machine learning techniques.

Explore why some problems are hard to solve but easy to check, including the famous P vs NP question. You will learn simple tools from circuit and proof complexity to tackle these challenges.

Investigate how modern hardware and systems make machine learning work in practice. From GPUs and TPUs to efficient ML techniques and large-scale deployment, you will get hands-on experience running models across cloud and edge environments.

This module covers the deep learning methods and systems architectures driving modern generative AI.